Sandblasting robot with model data-driven slip compensation and dual-motor coordinated hierarchical control method

Gravity and flexibility will cause fluctuations of the rotation angle in the servo system for flexible manipulators. The fluctuation will seriously affect the motion accuracy of end-effectors. Therefore, this paper adopts a control method combining the RBF (Radial Basis Function) neural network and pole placement strategy to suppress the rotation angle fluctuations. The RBF neural network is used to identify uncertain items caused by the manipulator’s flexibility and the time-varying characteristics of dynamic parameters. Besides, the pole placement strategy is used to optimize the PD (Proportional Differential) controller’s parameters to improve the response speed and stability. Firstly, a dynamic model of flexible manipulators considering gravity is established based on the assumed mode method and Lagrange’s principle. Then, the system’s control characteristics are analyzed, and the pole placement strategy optimizes the parameters of the PD controllers. Next, the control method based on the RBF neural network is proposed, and the Lyapunov stability theory demonstrates stability. Finally, numerical analysis and control experiments prove the effectiveness of the control method proposed in this paper. The means and standard deviations of rotation angle error are reduced by the control method. The results show that the control method can effectively reduce the rotation angle error and improve motion accuracy.

Hypertensive disorders present significant morbidity and mortality during pregnancy. Although ambulatory blood pressure measurement remains the standard of care for normotensive women, self-monitoring at home is increasingly prevalent. The widespread use of smartphones worldwide has sparked interest in mobile applications that leverage the built-in hardware for blood pressure estimation, yet few trials have assessed their accuracy. This prospective, longitudinal and monocentric study evaluated the accuracy of the OptiBP algorithm against standard oscillometric blood pressure measurements in a sample of pregnant women. Patients scheduled for elective caesarean sections were enrolled during the preoperative anesthesia consultations. Paired blood pressure measurements using OptiBP and the reference method were obtained at multiple time-points in late pregnancy and the postpartum period. Agreement between methods was assessed using the AAMI/ESH/ISO 81060-2:2018 standard thresholds of 5 ± 8 mmHg for mean ± standard deviation of the error (criterion 1) and patient-specific standard deviation of the mean error (criterion 2) and represented graphically by Bland-Altman scatterplots. Forty-eight women were enrolled of which 32 completed the protocol, yielding 338 total valid measurement pairs. Mean and standard deviation of the error were -1.78 ± 7.94 and 1.19 ± 7.59, and the patient-specific standard deviation of the mean error was 4.68 and 4.52, for SBP and DBP, respectively. Compared with blood pressure measurements taken with an oscillometric device, OptiBP's blood pressure estimates meet the AAMI/ESH/ISO 81060-2:2018 criteria.

Precise speed synchronization control of dual-motor and multimotor systems is essential in specific industrial applications. This article studies an improved data-driven speed synchronization predictive control (DSSPC) scheme of dual permanent magnet synchronous motors (PMSMs) powered by a five-leg inverter. Based on characteristic modeling theory, an equivalent first-order model is established for current prediction without motor parameter dependence. An improved model predictive control (MPC) with duty cycle adjustment is proposed, considering the switching constraint from the common leg. A new global cost function is formulated for the selection of optimal voltage vectors (VVs) of dual PMSMs. Moreover, a current reference compensation method is introduced to replace the traditional cross-coupling control with constant gain. It can quickly eliminate the synchronization error between dual PMSMs against disturbances. An extensive comparison with experimental tests is conducted between the proposed DSSPC scheme and the existing advanced approaches. The results prove that the proposed DSSPC strategy concurrently mitigates speed synchronization error and tracking error effectively, and with higher robustness against parameter mismatches and external disturbance.

An adaptive feedback control scheme and a switching strategy for the output tracking stabilization of switched nonlinear systems with time-delay are presented by using RBF (radial basis function) neural network The RBF neural networks is used to compensate adaptively for the unknown nonlinear parts of switched nonlinear systems, and the approximation errors of RBF neural networks are introduced to the adaptive law in order to improve the tracking quality of the systems. The controller and switching strategy can guarantee asymptotic stability of tracking error for the overall system

Modelling and simulation of fruit drop tests by discrete element method

SummaryThis paper addresses the neural network (NN) output feedback formation tracking control of nonholonomic wheeled mobile robots (WMRs) with limited voltage input. A desired formation is achieved based on the leader–follower strategy utilizing hyperbolic tangent saturation functions to reduce the risk of actuator saturation. The controller is developed by incorporating the high-gain observer and radial basis function (RBF) NNs using the inverse dynamics control technique. The high-gain observer is introduced to estimate velocities of the followers. The RBF NN preserves the robustness of the proposed controller against uncertain nonlinearities. The adaptive laws are also combined by a robust control term to estimate the weights of RBF NN, approximation errors, and bounds of unknown time-variant environmental disturbances. A Lyapunov-based stability analysis proves that all signals of the closed-loop system are bounded, and tracking errors are uniformly ultimately bounded. Finally, some simulations are carried out to show the effectiveness of the proposed controller for a number of WMRs.

This paper presents a robust adaptive sliding mode control strategy using radial basis function (RBF) neural network for a kind of value controlled asymmetric cylinder electro-hydraulic servo system of an ammunition transfer arm in the presence of uncertain nonlinearity and parameter uncertainty. On the premise of setting the expected trajectory, using RBF neural networks to approximate unknown parameters, by setting appropriate neural network parameters and adaptive terms, the change trend of position parameter was estimated. The stability of close loop system is verified by the Lyapounov theory. Compared with the simulation results of PID control method and expected trajectory, RBF Neural Net sliding mode control method has smaller system tracking error, faster response and better tracking performance.

Intelligent prediction for digging load of hydraulic excavators based on RBF neural network

The multiphase and multilevel motor drives have attracted more and more attention during the last decade since they offered advantages of high-power rating, lower voltage and current stresses in semiconductor switches, higher fault-tolerant ability, compared to the conventional two-level three-phase drives. This paper presents a model predictive current control (MPC) for a T-type three-level inverters fed dual three-phase permanent magnet synchronous motor (PMSM) drives. The key is to propose new space voltage vector synthesis method for voltage vector candidates in MPC scheme of the T-type three-level inverters fed dual three-phase PMSM drives. The proposed scheme can alleviate the computation burden of MPC and suppress the current harmonics induced from inherent property of dual three-phase PMSM. The experimental results are given to verify the validation of the proposed scheme.

Improvement of a pharmaceutical powder mixing process in a tote blender via DEM simulations

Biomass has potential as a carbon-neutral alternative to petroleum for chemical and energy products. However, complete replacement of fossil fuel is contingent upon efficient processes to eliminate undesirable characteristics of biomass, e.g., low bulk density, variability, and storage-induced quality problems. Mechanical size reduction via comminution is a processing operation to engineer favorable biomass flowability in handling. Crumbler rotary shear mill has been empirically demonstrated to produce more uniformly shaped particles with higher flowability than hammermilled biomass. This study combines modeling and experimentation to unveil fundamental understandings of the relation between granular particle characteristics and biomass flow behavior, which elucidate underlying mechanisms and guide selection of critical processing parameters. For this purpose, the impact of critical material attributes, including particle size (2–6 mm), particle shape (briquette, chip, clumped-sphere, cube, etc.), and surface roughness, on the angle of repose (AOR) of milled pine chips were investigated using discrete element method (DEM) simulations. Forest Concepts Crumbler rotary shear system is used to produce milled pine particles within the same size range considered in DEM simulations. AOR of different sets of these particles were measured experimentally to benchmark DEM results against experimental data. Specific energy consumption for the comminution of biomass with different particle size and moisture content are measured for technoeconomic analysis. Our results show that the smaller size (2 mm) of pine particle achieves better followability (i.e., smaller AOR) while the energy cost of comminution is significantly higher and bulk density is almost the same as the 6-mm pine particles. For the 2-mm particle size, Crumbles from veneer have better flow properties than Crumbles from chips. Contrarily, no significant difference was observed between the AOR of the two materials for the 6-mm particle size. Furthermore, from DEM simulations, mechanical interlocking between particles was found as a dominant factor in determining AOR of complex-shaped particles such as milled pine, which cannot be accurately captured by using simple particle shapes (e.g., mono-sphere) with a rolling resistance model. Conversely, clumped-sphere model alleviates this limitation without increasing computational cost significantly and can be used for accurate representation of biomass granular particles when simulating free-flow behavior.

The presence of overlarge gravel particles poses significant challenges for laboratory testing on prototype gravelly soils due to sample size limitations. To address this issue, replacement techniques, such as substituting overlarge particles with finer materials, offer practical solutions. However, the impact of these techniques on the mechanical behavior of gravelly soils, particularly shear strength and stiffness, remains poorly understood. This study aims to bridge this knowledge gap by investigating the particle size effect on the shear behaviors of binary mixtures using a series of Discrete Element Method (DEM) simulations. Updated scaling relations, based on Iai's generalized scaling relations, were proposed to correct for particle size effects. DEM simulations, including drained triaxial tests and shear modulus measurements, were performed to validate the proposed law. The results indicate that the gravel replacement technique has a minor effect on peak shear strength but significantly reduces soil stiffness, especially at high gravel contents. The scaling relations effectively correct for the particle size effect, enabling the accurate prediction of shear behaviors of the prototype gravelly soils from those of the model gravelly soils. These validations demonstrate that for addressing the soil deformation problem instead of the stability problem in ultimate state, the developed scaling relations are highly effective for correcting the particle size effect. Based on the developed scaling relations, engineers can predict prototype-scale shear behaviors of gravelly soils with overlarge particles using scaled laboratory models, reducing reliance on costly large-scale equipment. Additionally, future studies, through both DEM simulations and laboratory experiments, are recommended to further validate and refine the proposed method across diverse soil conditions and loading scenarios, such as cyclic loadings.

In simple shear testing, the specimen boundaries play a pivotal role in the transmission of shear forces. Waffle-style porous stones, plates with ribs or similar types of projections are used in experiments to reduce slippage at the boundary and transmit shear throughout the specimen. Conventionally, Discrete Element Method (DEM) simulations often model the top and bottom caps as flat boundaries with artificially enlarged friction coefficients, or use geometrical configurations that are computationally efficient. This research compares flat high-friction boundaries with ribbed and more novel-designed boundaries incorporating large pyramid, and small pyramid projections, aiming to improve shear transmission capability while ensuring computational efficiency. DEM simulations were conducted on specimens of steel bearings, with experimental validation using identical setups and particle properties. Specimens featuring different boundaries showed different void ratios in the layers close to the boundaries, with boundary effects diminishing towards the central zones. DEM simulations with projection boundaries demonstrated good agreement with experiments in terms of the macroscopic response, validating the effectiveness of the projection boundaries. The conventional flat boundaries exhibited limited shear transmission capability, resulting in insufficient development of shear stress and inadequate particle engagement. Conversely, projections on boundaries significantly improved shear stress transmission, ensuring the simple shear condition throughout the entire specimens. Projection boundaries introduced manageable increases in computational cost despite increased mesh complexity. This study highlights the importance of boundary design and recommends the adoption of projection-based boundaries in both experimental and numerical simple shear tests to ensure effective shear transmission and reliable results.

For the social robot with serial elastic actuator, the joint dynamics model has the problems of strong coupling and high nonlinear, and the traditional PD control algorithm cannot achieve accurate trajectory tracking effect on the joint position of social robot using series elastic actuator. Therefore, an optimized Radial basis function (RBF) neural network adaptive control algorithm was proposed. The method based on RBF neural network approximates the social robot joint model parameters, an adaptive law was designed to estimate the weights of the neural network and the joint model online. The dynamic plane method is combined to improve the robustness of the control algorithm. The simulation results show that the trajectory tracking error peak of PD control algorithm is 0.2 rad. Compared with PD control algorithm, the trajectory tracking error peak of RBF neural network adaptive control algorithm based on dynamic surface optimization is reduced to ± 0.05 rad, which realizes accurate approximation of the parameters of social robot joint model, and accurate dynamics model approximation provides a theoretical basis for further research on human–robot interaction (HRI) of social robots.

Precision contour tracking is one of the most important factors used to determine product quality in a machining tool. An interval type-2 fuzzy proportional–integral (PI) sliding mode control (IT2FPISMC) system is proposed herein to control the mover position of the two-axis motion stage with optical encoder sensors for trajectory feedback. A type-2 fuzzy method that can handle rule uncertainties is developed to approach the unknown nonlinear systems. The PI term is used to approximate the discontinuous control signal and mitigate the chattering phenomenon in the presence of unmodeled system dynamics and external disturbances. The adaptive control laws are derived based on the Lyapunov theorem, such that the closed-loop stability is guaranteed, and the output tracking errors of the system asymptotically converge to zero. A non-uniform rational B-spline interpolator with high accuracy is adopted in the biaxial linear stage. Moreover, typical circular, bowknot, heart, and star reference contours are tested. The results on the average tracking error and the tracking error standard deviation are experimented and compared to illustrate the performance of our proposed method. The tracking performance obtained from the IT2FPISMC method is better than that of the conventional method. Furthermore, the proposed method can achieve robustness for tracking different reference contours in industrial applications.